SAE using geospatial data

Nairobi Workshop: Day 4 (geospatial data)

Ann-Kristin Kreutzmann
Josh Merfeld

August 26, 2024

Introduction to geospatial data

  • One estimate says that 100 TB of only weather data are generated every single day
    • This means there is a lot of data to work with!
    • Note that this is also problematic, since it can be difficult to work with such large datasets
  • Geospatial data is used in a variety of fields
    • Agriculture
    • Urban planning
    • Environmental science
    • Public health
    • Transportation
    • And many more!

The amount of geospatial data is useful for SAE

  • Geospatial data can be highly predictive of e.g. poverty
    • Urbanity
    • Land class/cover
    • Vegetation indices
    • Population counts
    • etc. etc.
  • More importantly: it’s available everywhere!

Think of what you need for SAE

  • You need a sample, e.g. a household survey
    • This will only cover some of the country

  • You need auxiliary data that is:
    • Predictive of the outcome you care about
    • Available throughout the entire country

  • Some countries, use administrative data
    • But, importantly, it’s often not available or is of low quality!

A quick example

  • Let’s take a look at Malawi

  • Why Malawi?

    • I have survey data you can use 😃
    • Only going to use part of Malawi for this example (size of data)
  • Consider the 2019/2020 Integrated Household Survey (IHS5)

    • Was used for the Malawi Poverty Report 2020
    • Can say things about poverty at the district level
    • If you want to split by urban/rural, only at the region level

A quick example

Malawi admin areas - Northern region only

  • Survey only lets us say things about the districts!
  • What if we want to say something about traditional authorities (TAs)?
  • Individual TAs might not have enough observations
  • We could use SAE! But what auxiliary data?

Observations at the district and TA level

Sub-area model with sectors

  • One option: estimate a sub-area model at the EA level!

  • Steps:

    • Collapse survey data to the EA level
    • Extract geospatial data at the EA level
    • Estimate the model

Getting started with
geospatial data

Getting started with geospatial data

  • Due to time, this introduction will be necessarily brief

  • We are going to learn the following:

    • Shapefiles
    • Rasters
    • Extracting data